Abstract 943: Informatics-based and other approaches to estimate real world disease progression in lung cancer
Authors: Rossybelle P. Amorrortu, Yayi Zhao, Matthew B. Schabath, Dana E. Rollison
Journal: Cancer Research (2023)

Abstract Background: Randomized clinical trials (RCT’s) of novel treatments for solid tumors typically measure disease progression using the Response Evaluation Criteria in Solid Tumors (RECIST) with precisely timed patient assessments relative to treatment administration. However, outside of RCTs, patient assessments are not precisely timed, uniformly documented or manually curated, introducing difficulties in deriving cancer outcomes using real-world data (RWD). Novel informatics-based approaches to estimate cancer outcomes are needed to advance the use of RWD for facilitating rigorous cancer research. The goal of this study was to report on current methodologic approaches to measure (or approximate) real-world (RW) disease progression using lung cancer as an illustrative example. Methods: A methodical literature review was conducted using PubMed to identify approaches to estimate/approximate RW-progression among patients with lung cancer. Between 2008-2022, a total of 40 articles were identified relating to the estimation of lung cancer RW-progression. Data abstracted during the review included data source type, approach used to estimate RW-progression, and whether the RW-endpoints were compared to a gold standard, and how the gold standard was defined. Results: Data sources used to estimate RW-progression within the various methods included clinical notes, medication information, pathology/radiology reports, and radiological images. A total of five different methods to estimate RW-progression were identified from the search results, including studies reporting manual abstraction (n=17), treatment-based algorithms (n=6), natural language processing (n=4), algorithms based on changes in tumor volume (n=7), and radiomics (n=6). Comparisons of RW-progression endpoints to a “gold standard” were used by 50% of studies although the definitions of the gold standard varied across studies. As a final step, a comparison of the RW-progression endpoints will be presented. Conclusions: A substantial amount of literature exists relating to methods for curating RW-disease progression in patients with lung cancer. However, comparison of methods and results across studies is challenging due to the lack of a consistent gold standard. Furthermore, no studies assessed a variety of methods within the same lung cancer patient population. Studies are needed that compare several methods within the same patient population in order to identify an optimal method for estimating RW-progression with high effectiveness and accuracy. Lastly, concerted efforts are needed to define a gold standard and quality metrics for RWD. Citation Format: Rossybelle P. Amorrortu, Yayi Zhao, Matthew B. Schabath, Dana E. Rollison. Informatics-based and other approaches to estimate real world disease progression in lung cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 943.